{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对广告效果分析实现有针对性的广告效果测量和优化工作."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "import matplotlib as mpl \n",
    "import matplotlib.pyplot as plt \n",
    "from sklearn.preprocessing import MinMaxScaler,OneHotEncoder,StandardScaler\n",
    "from sklearn.metrics import silhouette_score # 导入轮廓系数指标\n",
    "from sklearn.cluster import KMeans # KMeans模块\n",
    "import warnings\n",
    "## 设置属性防止中文乱码\n",
    "mpl.rcParams['font.sans-serif'] = [u'SimHei']\n",
    "mpl.rcParams['axes.unicode_minus'] = False\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('./datas/ad_performance.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>渠道代号</th>\n",
       "      <th>日均UV</th>\n",
       "      <th>平均注册率</th>\n",
       "      <th>平均搜索量</th>\n",
       "      <th>访问深度</th>\n",
       "      <th>平均停留时间</th>\n",
       "      <th>订单转化率</th>\n",
       "      <th>投放总时间</th>\n",
       "      <th>素材类型</th>\n",
       "      <th>广告类型</th>\n",
       "      <th>合作方式</th>\n",
       "      <th>广告尺寸</th>\n",
       "      <th>广告卖点</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>A203</td>\n",
       "      <td>3.69</td>\n",
       "      <td>0.0071</td>\n",
       "      <td>0.0214</td>\n",
       "      <td>2.3071</td>\n",
       "      <td>419.77</td>\n",
       "      <td>0.0258</td>\n",
       "      <td>20</td>\n",
       "      <td>jpg</td>\n",
       "      <td>banner</td>\n",
       "      <td>roi</td>\n",
       "      <td>140*40</td>\n",
       "      <td>打折</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>A387</td>\n",
       "      <td>178.70</td>\n",
       "      <td>0.0040</td>\n",
       "      <td>0.0324</td>\n",
       "      <td>2.0489</td>\n",
       "      <td>157.94</td>\n",
       "      <td>0.0030</td>\n",
       "      <td>19</td>\n",
       "      <td>jpg</td>\n",
       "      <td>banner</td>\n",
       "      <td>cpc</td>\n",
       "      <td>140*40</td>\n",
       "      <td>满减</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>A388</td>\n",
       "      <td>91.77</td>\n",
       "      <td>0.0022</td>\n",
       "      <td>0.0530</td>\n",
       "      <td>1.8771</td>\n",
       "      <td>357.93</td>\n",
       "      <td>0.0026</td>\n",
       "      <td>4</td>\n",
       "      <td>jpg</td>\n",
       "      <td>banner</td>\n",
       "      <td>cpc</td>\n",
       "      <td>140*40</td>\n",
       "      <td>满减</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>A389</td>\n",
       "      <td>1.09</td>\n",
       "      <td>0.0074</td>\n",
       "      <td>0.3382</td>\n",
       "      <td>4.2426</td>\n",
       "      <td>364.07</td>\n",
       "      <td>0.0153</td>\n",
       "      <td>10</td>\n",
       "      <td>jpg</td>\n",
       "      <td>banner</td>\n",
       "      <td>cpc</td>\n",
       "      <td>140*40</td>\n",
       "      <td>满减</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>A390</td>\n",
       "      <td>3.37</td>\n",
       "      <td>0.0028</td>\n",
       "      <td>0.1740</td>\n",
       "      <td>2.1934</td>\n",
       "      <td>313.34</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>30</td>\n",
       "      <td>jpg</td>\n",
       "      <td>banner</td>\n",
       "      <td>cpc</td>\n",
       "      <td>140*40</td>\n",
       "      <td>满减</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  渠道代号    日均UV   平均注册率   平均搜索量    访问深度  平均停留时间   订单转化率  投放总时间  \\\n",
       "0           0  A203    3.69  0.0071  0.0214  2.3071  419.77  0.0258     20   \n",
       "1           1  A387  178.70  0.0040  0.0324  2.0489  157.94  0.0030     19   \n",
       "2           2  A388   91.77  0.0022  0.0530  1.8771  357.93  0.0026      4   \n",
       "3           3  A389    1.09  0.0074  0.3382  4.2426  364.07  0.0153     10   \n",
       "4           4  A390    3.37  0.0028  0.1740  2.1934  313.34  0.0007     30   \n",
       "\n",
       "  素材类型    广告类型 合作方式    广告尺寸 广告卖点  \n",
       "0  jpg  banner  roi  140*40   打折  \n",
       "1  jpg  banner  cpc  140*40   满减  \n",
       "2  jpg  banner  cpc  140*40   满减  \n",
       "3  jpg  banner  cpc  140*40   满减  \n",
       "4  jpg  banner  cpc  140*40   满减  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 889 entries, 0 to 888\n",
      "Data columns (total 14 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   Unnamed: 0  889 non-null    int64  \n",
      " 1   渠道代号        889 non-null    object \n",
      " 2   日均UV        889 non-null    float64\n",
      " 3   平均注册率       889 non-null    float64\n",
      " 4   平均搜索量       889 non-null    float64\n",
      " 5   访问深度        889 non-null    float64\n",
      " 6   平均停留时间      887 non-null    float64\n",
      " 7   订单转化率       889 non-null    float64\n",
      " 8   投放总时间       889 non-null    int64  \n",
      " 9   素材类型        889 non-null    object \n",
      " 10  广告类型        889 non-null    object \n",
      " 11  合作方式        889 non-null    object \n",
      " 12  广告尺寸        889 non-null    object \n",
      " 13  广告卖点        889 non-null    object \n",
      "dtypes: float64(6), int64(2), object(6)\n",
      "memory usage: 97.4+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop('Unnamed: 0',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 探索性分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>日均UV</th>\n",
       "      <td>889.0</td>\n",
       "      <td>540.85</td>\n",
       "      <td>1634.41</td>\n",
       "      <td>0.06</td>\n",
       "      <td>6.18</td>\n",
       "      <td>114.18</td>\n",
       "      <td>466.87</td>\n",
       "      <td>25294.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>平均注册率</th>\n",
       "      <td>889.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>平均搜索量</th>\n",
       "      <td>889.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>访问深度</th>\n",
       "      <td>889.0</td>\n",
       "      <td>2.17</td>\n",
       "      <td>3.80</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.39</td>\n",
       "      <td>1.79</td>\n",
       "      <td>2.22</td>\n",
       "      <td>98.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>平均停留时间</th>\n",
       "      <td>887.0</td>\n",
       "      <td>262.67</td>\n",
       "      <td>224.36</td>\n",
       "      <td>1.64</td>\n",
       "      <td>126.02</td>\n",
       "      <td>236.55</td>\n",
       "      <td>357.98</td>\n",
       "      <td>4450.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>订单转化率</th>\n",
       "      <td>889.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>投放总时间</th>\n",
       "      <td>889.0</td>\n",
       "      <td>16.05</td>\n",
       "      <td>8.51</td>\n",
       "      <td>1.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>24.00</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        count    mean      std   min     25%     50%     75%       max\n",
       "日均UV    889.0  540.85  1634.41  0.06    6.18  114.18  466.87  25294.77\n",
       "平均注册率   889.0    0.00     0.00  0.00    0.00    0.00    0.00      0.04\n",
       "平均搜索量   889.0    0.03     0.11  0.00    0.00    0.00    0.01      1.04\n",
       "访问深度    889.0    2.17     3.80  1.00    1.39    1.79    2.22     98.98\n",
       "平均停留时间  887.0  262.67   224.36  1.64  126.02  236.55  357.98   4450.83\n",
       "订单转化率   889.0    0.00     0.01  0.00    0.00    0.00    0.00      0.22\n",
       "投放总时间   889.0   16.05     8.51  1.00    9.00   16.00   24.00     30.00"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe().round(2).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 分布分析\n",
    "len(data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x6000 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "fig = plt.figure(num=1,figsize=(10,60))\n",
    "\n",
    "l1 = data.columns\n",
    "for c in range(1,len(l1)):\n",
    "    ax = fig.add_subplot(13,1,c)\n",
    "    sns.histplot(data[l1[c]]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# numeric_only=True 只挑选数据类型为number类型的列进行相关性分析 \n",
    "# 相关性分析\n",
    "data.corr(numeric_only=True).round(2).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 相关性可视化展示\n",
    "import seaborn as sns \n",
    "plt.figure(figsize=(6,5),dpi=100)\n",
    "corr = data.corr(numeric_only=True).round(2)\n",
    "sns.heatmap(corr,cmap='Reds',annot = True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1 删除平均平均停留时间列\n",
    "data2 = data.drop(['平均停留时间'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "素材类型-的取值有：\n",
      "['jpg' 'swf' 'gif' 'sp']\n",
      "广告类型-的取值有：\n",
      "['banner' 'tips' '不确定' '横幅' '暂停']\n",
      "合作方式-的取值有：\n",
      "['roi' 'cpc' 'cpm' 'cpd']\n",
      "广告尺寸-的取值有：\n",
      "['140*40' '308*388' '450*300' '600*90' '480*360' '960*126' '900*120'\n",
      " '390*270']\n",
      "广告卖点-的取值有：\n",
      "['打折' '满减' '满赠' '秒杀' '直降' '满返']\n"
     ]
    }
   ],
   "source": [
    "#2 类别变量取值\n",
    "cols=[\"素材类型\",\"广告类型\",\"合作方式\",\"广告尺寸\",\"广告卖点\"]\n",
    "for x in cols:\n",
    "    data=data2[x].unique()\n",
    "    print(\"{0}-的取值有：\\n{1}\".format(x,data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 1. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0.\n",
      "  0. 0. 0.]\n",
      " [0. 1. 0. 0. 1. 0. 0. 0. 0. 1. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.\n",
      "  0. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "# 3 字符串分类独热编码处理\n",
    "cols = ['素材类型','广告类型','合作方式','广告尺寸','广告卖点'] \n",
    "model_ohe = OneHotEncoder(sparse=False)  # 建立OneHotEncode对象\n",
    "ohe_matrix = model_ohe.fit_transform(data2[cols])  # 直接转换\n",
    "print(ohe_matrix[:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>素材类型_gif</th>\n",
       "      <th>素材类型_jpg</th>\n",
       "      <th>素材类型_sp</th>\n",
       "      <th>素材类型_swf</th>\n",
       "      <th>广告类型_banner</th>\n",
       "      <th>广告类型_tips</th>\n",
       "      <th>广告类型_不确定</th>\n",
       "      <th>广告类型_暂停</th>\n",
       "      <th>广告类型_横幅</th>\n",
       "      <th>合作方式_cpc</th>\n",
       "      <th>...</th>\n",
       "      <th>广告尺寸_480*360</th>\n",
       "      <th>广告尺寸_600*90</th>\n",
       "      <th>广告尺寸_900*120</th>\n",
       "      <th>广告尺寸_960*126</th>\n",
       "      <th>广告卖点_打折</th>\n",
       "      <th>广告卖点_满减</th>\n",
       "      <th>广告卖点_满赠</th>\n",
       "      <th>广告卖点_满返</th>\n",
       "      <th>广告卖点_直降</th>\n",
       "      <th>广告卖点_秒杀</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   素材类型_gif  素材类型_jpg  素材类型_sp  素材类型_swf  广告类型_banner  广告类型_tips  广告类型_不确定  \\\n",
       "0         0         1        0         0            1          0         0   \n",
       "1         0         1        0         0            1          0         0   \n",
       "2         0         1        0         0            1          0         0   \n",
       "3         0         1        0         0            1          0         0   \n",
       "4         0         1        0         0            1          0         0   \n",
       "\n",
       "   广告类型_暂停  广告类型_横幅  合作方式_cpc  ...  广告尺寸_480*360  广告尺寸_600*90  广告尺寸_900*120  \\\n",
       "0        0        0         0  ...             0            0             0   \n",
       "1        0        0         1  ...             0            0             0   \n",
       "2        0        0         1  ...             0            0             0   \n",
       "3        0        0         1  ...             0            0             0   \n",
       "4        0        0         1  ...             0            0             0   \n",
       "\n",
       "   广告尺寸_960*126  广告卖点_打折  广告卖点_满减  广告卖点_满赠  广告卖点_满返  广告卖点_直降  广告卖点_秒杀  \n",
       "0             0        1        0        0        0        0        0  \n",
       "1             0        0        1        0        0        0        0  \n",
       "2             0        0        1        0        0        0        0  \n",
       "3             0        0        1        0        0        0        0  \n",
       "4             0        0        1        0        0        0        0  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 用pandas的方法\n",
    "ohe_matrix1=pd.get_dummies(data2[cols])\n",
    "ohe_matrix1.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.   0.18 0.02 0.01 0.12 0.66]\n",
      " [0.01 0.1  0.03 0.01 0.01 0.62]\n",
      " [0.   0.06 0.05 0.01 0.01 0.1 ]\n",
      " ...\n",
      " [0.01 0.01 0.   0.   0.   0.72]\n",
      " [0.05 0.   0.   0.   0.   0.31]\n",
      " [0.   0.   0.   0.53 0.   0.62]]\n"
     ]
    }
   ],
   "source": [
    "# 数据标准化\n",
    "sacle_matrix = data2.iloc[:, 1:7]  # 获得要转换的矩阵\n",
    "model_scaler = MinMaxScaler()  # 建立MinMaxScaler模型对象\n",
    "data_scaled = model_scaler.fit_transform(sacle_matrix)  # MinMaxScaler标准化处理\n",
    "print(data_scaled.round(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 合并所有维度\n",
    "X = np.hstack((data_scaled, ohe_matrix))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.4.3\n"
     ]
    }
   ],
   "source": [
    "print(pd.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 建立模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*************************K值对应的轮廓系数:*************************\n",
      "[[2.         0.38655493]\n",
      " [3.         0.45757883]\n",
      " [4.         0.50209812]\n",
      " [5.         0.4800359 ]\n",
      " [6.         0.50188479]\n",
      " [7.         0.48317001]]\n",
      "最优的K值是:4 \n",
      "对应的轮廓系数是:0.5020981194788053\n"
     ]
    }
   ],
   "source": [
    "score_list = []  # 用来存储每个K下模型的平局轮廓系数\n",
    "silhouette_int = -1  # 初始化的平均轮廓系数阀值\n",
    "for n_clusters in range(2, 8):  # 遍历从2到5几个有限组\n",
    "    model_kmeans = KMeans(n_clusters=n_clusters)  # 建立聚类模型对象\n",
    "    labels_tmp = model_kmeans.fit_predict(X)  # 训练聚类模型\n",
    "    silhouette_tmp = silhouette_score(X, labels_tmp)  # 得到每个K下的平均轮廓系数\n",
    "    if silhouette_tmp > silhouette_int:  # 如果平均轮廓系数更高\n",
    "        best_k = n_clusters  # 保存K将最好的K存储下来\n",
    "        silhouette_int = silhouette_tmp  # 保存平均轮廓得分\n",
    "        best_kmeans = model_kmeans  # 保存模型实例对象\n",
    "        cluster_labels_k = labels_tmp  # 保存聚类标签\n",
    "    score_list.append([n_clusters, silhouette_tmp])  # 将每次K及其得分追加到列表\n",
    "print('{:*^60}'.format('K值对应的轮廓系数:'))\n",
    "print(np.array(score_list))  # 打印输出所有K下的详细得分\n",
    "print('最优的K值是:{0} \\n对应的轮廓系数是:{1}'.format(best_k, silhouette_int))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型运行时间: 0.03592085838317871\n",
      "轮廓系数:0.501362347246509\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import AgglomerativeClustering\n",
    "import time\n",
    "\n",
    "ac = AgglomerativeClustering(n_clusters=4,linkage='ward')\n",
    "t0 = time.time()\n",
    "ac.fit(X)\n",
    "fit_time = time.time() - t0\n",
    "print('模型运行时间:',fit_time)\n",
    "\n",
    "ac_label = ac.fit_predict(X)\n",
    "ac_score = silhouette_score(X,ac_label)\n",
    "print(f'轮廓系数:{ac_score}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>渠道代号</th>\n",
       "      <th>日均UV</th>\n",
       "      <th>平均注册率</th>\n",
       "      <th>平均搜索量</th>\n",
       "      <th>访问深度</th>\n",
       "      <th>订单转化率</th>\n",
       "      <th>投放总时间</th>\n",
       "      <th>素材类型</th>\n",
       "      <th>广告类型</th>\n",
       "      <th>合作方式</th>\n",
       "      <th>广告尺寸</th>\n",
       "      <th>广告卖点</th>\n",
       "      <th>clusters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A203</td>\n",
       "      <td>3.69</td>\n",
       "      <td>0.0071</td>\n",
       "      <td>0.0214</td>\n",
       "      <td>2.3071</td>\n",
       "      <td>0.0258</td>\n",
       "      <td>20</td>\n",
       "      <td>jpg</td>\n",
       "      <td>banner</td>\n",
       "      <td>roi</td>\n",
       "      <td>140*40</td>\n",
       "      <td>打折</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A387</td>\n",
       "      <td>178.70</td>\n",
       "      <td>0.0040</td>\n",
       "      <td>0.0324</td>\n",
       "      <td>2.0489</td>\n",
       "      <td>0.0030</td>\n",
       "      <td>19</td>\n",
       "      <td>jpg</td>\n",
       "      <td>banner</td>\n",
       "      <td>cpc</td>\n",
       "      <td>140*40</td>\n",
       "      <td>满减</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A388</td>\n",
       "      <td>91.77</td>\n",
       "      <td>0.0022</td>\n",
       "      <td>0.0530</td>\n",
       "      <td>1.8771</td>\n",
       "      <td>0.0026</td>\n",
       "      <td>4</td>\n",
       "      <td>jpg</td>\n",
       "      <td>banner</td>\n",
       "      <td>cpc</td>\n",
       "      <td>140*40</td>\n",
       "      <td>满减</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A389</td>\n",
       "      <td>1.09</td>\n",
       "      <td>0.0074</td>\n",
       "      <td>0.3382</td>\n",
       "      <td>4.2426</td>\n",
       "      <td>0.0153</td>\n",
       "      <td>10</td>\n",
       "      <td>jpg</td>\n",
       "      <td>banner</td>\n",
       "      <td>cpc</td>\n",
       "      <td>140*40</td>\n",
       "      <td>满减</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A390</td>\n",
       "      <td>3.37</td>\n",
       "      <td>0.0028</td>\n",
       "      <td>0.1740</td>\n",
       "      <td>2.1934</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>30</td>\n",
       "      <td>jpg</td>\n",
       "      <td>banner</td>\n",
       "      <td>cpc</td>\n",
       "      <td>140*40</td>\n",
       "      <td>满减</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   渠道代号    日均UV   平均注册率   平均搜索量    访问深度   订单转化率  投放总时间 素材类型    广告类型 合作方式  \\\n",
       "0  A203    3.69  0.0071  0.0214  2.3071  0.0258     20  jpg  banner  roi   \n",
       "1  A387  178.70  0.0040  0.0324  2.0489  0.0030     19  jpg  banner  cpc   \n",
       "2  A388   91.77  0.0022  0.0530  1.8771  0.0026      4  jpg  banner  cpc   \n",
       "3  A389    1.09  0.0074  0.3382  4.2426  0.0153     10  jpg  banner  cpc   \n",
       "4  A390    3.37  0.0028  0.1740  2.1934  0.0007     30  jpg  banner  cpc   \n",
       "\n",
       "     广告尺寸 广告卖点  clusters  \n",
       "0  140*40   打折         0  \n",
       "1  140*40   满减         0  \n",
       "2  140*40   满减         0  \n",
       "3  140*40   满减         0  \n",
       "4  140*40   满减         0  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将原始数据与聚类标签整合\n",
    "cluster_labels = pd.DataFrame(cluster_labels_k, columns=['clusters'])  # 获得训练集下的标签信息\n",
    "merge_data = pd.concat((data2, cluster_labels), axis=1)  # 将原始处理过的数据跟聚类标签整合\n",
    "merge_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>数量</th>\n",
       "      <th>比例</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>349</td>\n",
       "      <td>0.392576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>313</td>\n",
       "      <td>0.352081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>154</td>\n",
       "      <td>0.173228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>73</td>\n",
       "      <td>0.082115</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    数量        比例\n",
       "2  349  0.392576\n",
       "1  313  0.352081\n",
       "0  154  0.173228\n",
       "3   73  0.082115"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 类别数量\n",
    "s1 = merge_data['clusters'].value_counts()\n",
    "# 类别占比\n",
    "s2 = merge_data['clusters'].value_counts() / merge_data.shape[0]\n",
    "\n",
    "df1 = pd.concat([s1,s2],axis=1)\n",
    "df1.columns=['数量','比例']\n",
    "df1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>clusters</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">日均UV</th>\n",
       "      <th>count</th>\n",
       "      <td>154.000000</td>\n",
       "      <td>313.000000</td>\n",
       "      <td>349.000000</td>\n",
       "      <td>73.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>613.835779</td>\n",
       "      <td>572.521054</td>\n",
       "      <td>300.205415</td>\n",
       "      <td>1401.524521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2717.418784</td>\n",
       "      <td>1390.013222</td>\n",
       "      <td>933.015250</td>\n",
       "      <td>1904.371123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.530000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.620000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.807500</td>\n",
       "      <td>20.940000</td>\n",
       "      <td>6.520000</td>\n",
       "      <td>73.630000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>14.180000</td>\n",
       "      <td>204.190000</td>\n",
       "      <td>88.940000</td>\n",
       "      <td>699.720000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>124.190000</td>\n",
       "      <td>604.840000</td>\n",
       "      <td>285.420000</td>\n",
       "      <td>1688.730000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>25294.770000</td>\n",
       "      <td>16516.130000</td>\n",
       "      <td>12586.930000</td>\n",
       "      <td>9276.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">平均注册率</th>\n",
       "      <th>count</th>\n",
       "      <td>154.000000</td>\n",
       "      <td>313.000000</td>\n",
       "      <td>349.000000</td>\n",
       "      <td>73.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.002649</td>\n",
       "      <td>0.001182</td>\n",
       "      <td>0.001099</td>\n",
       "      <td>0.001229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.004656</td>\n",
       "      <td>0.003215</td>\n",
       "      <td>0.002576</td>\n",
       "      <td>0.002576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.001450</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.003575</td>\n",
       "      <td>0.000400</td>\n",
       "      <td>0.001100</td>\n",
       "      <td>0.000800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.039100</td>\n",
       "      <td>0.024500</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.011800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">平均搜索量</th>\n",
       "      <th>count</th>\n",
       "      <td>154.000000</td>\n",
       "      <td>313.000000</td>\n",
       "      <td>349.000000</td>\n",
       "      <td>73.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.020118</td>\n",
       "      <td>0.051364</td>\n",
       "      <td>0.015780</td>\n",
       "      <td>0.033232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.051342</td>\n",
       "      <td>0.151605</td>\n",
       "      <td>0.063568</td>\n",
       "      <td>0.105772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.003525</td>\n",
       "      <td>0.000200</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.000800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.009750</td>\n",
       "      <td>0.001800</td>\n",
       "      <td>0.003500</td>\n",
       "      <td>0.002200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.016650</td>\n",
       "      <td>0.014700</td>\n",
       "      <td>0.009500</td>\n",
       "      <td>0.004200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.415500</td>\n",
       "      <td>1.037000</td>\n",
       "      <td>0.582600</td>\n",
       "      <td>0.584100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">访问深度</th>\n",
       "      <th>count</th>\n",
       "      <td>154.000000</td>\n",
       "      <td>313.000000</td>\n",
       "      <td>349.000000</td>\n",
       "      <td>73.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.189583</td>\n",
       "      <td>2.144642</td>\n",
       "      <td>2.269508</td>\n",
       "      <td>1.727166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.947175</td>\n",
       "      <td>1.167923</td>\n",
       "      <td>5.916406</td>\n",
       "      <td>0.943332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.053100</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.007400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.670075</td>\n",
       "      <td>1.522400</td>\n",
       "      <td>1.378800</td>\n",
       "      <td>1.223000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.038100</td>\n",
       "      <td>1.860200</td>\n",
       "      <td>1.693800</td>\n",
       "      <td>1.393900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.506725</td>\n",
       "      <td>2.251800</td>\n",
       "      <td>1.978500</td>\n",
       "      <td>1.709900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.958300</td>\n",
       "      <td>10.121100</td>\n",
       "      <td>98.979900</td>\n",
       "      <td>5.849700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">订单转化率</th>\n",
       "      <th>count</th>\n",
       "      <td>154.000000</td>\n",
       "      <td>313.000000</td>\n",
       "      <td>349.000000</td>\n",
       "      <td>73.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.003309</td>\n",
       "      <td>0.003868</td>\n",
       "      <td>0.001817</td>\n",
       "      <td>0.002475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.006901</td>\n",
       "      <td>0.017208</td>\n",
       "      <td>0.006311</td>\n",
       "      <td>0.008638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000225</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.001900</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.000300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.003375</td>\n",
       "      <td>0.000900</td>\n",
       "      <td>0.001700</td>\n",
       "      <td>0.000700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.071900</td>\n",
       "      <td>0.216500</td>\n",
       "      <td>0.088200</td>\n",
       "      <td>0.063800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">投放总时间</th>\n",
       "      <th>count</th>\n",
       "      <td>154.000000</td>\n",
       "      <td>313.000000</td>\n",
       "      <td>349.000000</td>\n",
       "      <td>73.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>15.681818</td>\n",
       "      <td>17.124601</td>\n",
       "      <td>15.349570</td>\n",
       "      <td>15.602740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.528847</td>\n",
       "      <td>8.199074</td>\n",
       "      <td>8.770272</td>\n",
       "      <td>8.217089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>23.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "clusters                0             1             2            3\n",
       "日均UV  count    154.000000    313.000000    349.000000    73.000000\n",
       "      mean     613.835779    572.521054    300.205415  1401.524521\n",
       "      std     2717.418784   1390.013222    933.015250  1904.371123\n",
       "      min        0.530000      0.070000      0.060000     0.620000\n",
       "      25%        1.807500     20.940000      6.520000    73.630000\n",
       "      50%       14.180000    204.190000     88.940000   699.720000\n",
       "      75%      124.190000    604.840000    285.420000  1688.730000\n",
       "      max    25294.770000  16516.130000  12586.930000  9276.450000\n",
       "平均注册率 count    154.000000    313.000000    349.000000    73.000000\n",
       "      mean       0.002649      0.001182      0.001099     0.001229\n",
       "      std        0.004656      0.003215      0.002576     0.002576\n",
       "      min        0.000000      0.000000      0.000000     0.000000\n",
       "      25%        0.000000      0.000000      0.000000     0.000000\n",
       "      50%        0.001450      0.000000      0.000000     0.000200\n",
       "      75%        0.003575      0.000400      0.001100     0.000800\n",
       "      max        0.039100      0.024500      0.025000     0.011800\n",
       "平均搜索量 count    154.000000    313.000000    349.000000    73.000000\n",
       "      mean       0.020118      0.051364      0.015780     0.033232\n",
       "      std        0.051342      0.151605      0.063568     0.105772\n",
       "      min        0.000000      0.000000      0.000000     0.000000\n",
       "      25%        0.003525      0.000200      0.001000     0.000800\n",
       "      50%        0.009750      0.001800      0.003500     0.002200\n",
       "      75%        0.016650      0.014700      0.009500     0.004200\n",
       "      max        0.415500      1.037000      0.582600     0.584100\n",
       "访问深度  count    154.000000    313.000000    349.000000    73.000000\n",
       "      mean       2.189583      2.144642      2.269508     1.727166\n",
       "      std        0.947175      1.167923      5.916406     0.943332\n",
       "      min        1.053100      1.000000      1.000000     1.007400\n",
       "      25%        1.670075      1.522400      1.378800     1.223000\n",
       "      50%        2.038100      1.860200      1.693800     1.393900\n",
       "      75%        2.506725      2.251800      1.978500     1.709900\n",
       "      max        9.958300     10.121100     98.979900     5.849700\n",
       "订单转化率 count    154.000000    313.000000    349.000000    73.000000\n",
       "      mean       0.003309      0.003868      0.001817     0.002475\n",
       "      std        0.006901      0.017208      0.006311     0.008638\n",
       "      min        0.000000      0.000000      0.000000     0.000000\n",
       "      25%        0.000225      0.000000      0.000000     0.000100\n",
       "      50%        0.001900      0.000100      0.000100     0.000300\n",
       "      75%        0.003375      0.000900      0.001700     0.000700\n",
       "      max        0.071900      0.216500      0.088200     0.063800\n",
       "投放总时间 count    154.000000    313.000000    349.000000    73.000000\n",
       "      mean      15.681818     17.124601     15.349570    15.602740\n",
       "      std        8.528847      8.199074      8.770272     8.217089\n",
       "      min        1.000000      1.000000      1.000000     1.000000\n",
       "      25%        9.000000     11.000000      8.000000     9.000000\n",
       "      50%       15.000000     17.000000     16.000000    16.000000\n",
       "      75%       23.000000     24.000000     23.000000    23.000000\n",
       "      max       30.000000     30.000000     30.000000    30.000000"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取数值类别的描述性统计\n",
    "merge_data.groupby('clusters').describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>日均UV</th>\n",
       "      <th>平均注册率</th>\n",
       "      <th>平均搜索量</th>\n",
       "      <th>访问深度</th>\n",
       "      <th>订单转化率</th>\n",
       "      <th>投放总时间</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clusters</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>613.835779</td>\n",
       "      <td>0.002649</td>\n",
       "      <td>0.020118</td>\n",
       "      <td>2.189583</td>\n",
       "      <td>0.003309</td>\n",
       "      <td>15.681818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>572.521054</td>\n",
       "      <td>0.001182</td>\n",
       "      <td>0.051364</td>\n",
       "      <td>2.144642</td>\n",
       "      <td>0.003868</td>\n",
       "      <td>17.124601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>300.205415</td>\n",
       "      <td>0.001099</td>\n",
       "      <td>0.015780</td>\n",
       "      <td>2.269508</td>\n",
       "      <td>0.001817</td>\n",
       "      <td>15.349570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1401.524521</td>\n",
       "      <td>0.001229</td>\n",
       "      <td>0.033232</td>\n",
       "      <td>1.727166</td>\n",
       "      <td>0.002475</td>\n",
       "      <td>15.602740</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 日均UV     平均注册率     平均搜索量      访问深度     订单转化率      投放总时间\n",
       "clusters                                                                \n",
       "0          613.835779  0.002649  0.020118  2.189583  0.003309  15.681818\n",
       "1          572.521054  0.001182  0.051364  2.144642  0.003868  17.124601\n",
       "2          300.205415  0.001099  0.015780  2.269508  0.001817  15.349570\n",
       "3         1401.524521  0.001229  0.033232  1.727166  0.002475  15.602740"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = merge_data.groupby('clusters').mean(numeric_only=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.28477701 1.         0.12190332 0.85263014 0.72741356 0.18717875]\n",
      " [0.24726316 0.05355178 1.         0.76976618 1.         1.        ]\n",
      " [0.         0.         0.         1.         0.         0.        ]\n",
      " [1.         0.08344939 0.49043321 0.         0.32105423 0.14262827]]\n"
     ]
    }
   ],
   "source": [
    "new_df = MinMaxScaler().fit_transform(df)\n",
    "print(new_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 参数设置\n",
    "# 角度lable\n",
    "radar_labels=df.columns\n",
    "# lable个数\n",
    "nAttr=6\n",
    "#数据值\n",
    "data=new_df.T \n",
    "# 类别名称\n",
    "data_labels=df.index\n",
    "\n",
    "\n",
    "# 设置角度\n",
    "angles=np.linspace(0,2*np.pi,nAttr,endpoint= False)\n",
    "data=np.concatenate((data, [data[0]])) \n",
    "\n",
    "angles=np.concatenate((angles, [angles[0]]))\n",
    "# 设置画布\n",
    "fig=plt.figure(facecolor=\"white\",figsize=(10,6))\n",
    "plt.subplot(111, polar=True)\n",
    "# 绘图\n",
    "plt.plot(angles,data,'o-',linewidth=1.5,alpha=1)\n",
    "# 填充颜色\n",
    "plt.fill(angles,data, alpha=0.3) \n",
    "plt.thetagrids(angles[:-1]*180/np.pi, radar_labels,1.2) \n",
    "plt.figtext(0.52, 0.95,'类别特征', \n",
    "            ha='center', size=20)\n",
    "# 设置图例\n",
    "legend=plt.legend(data_labels, \n",
    "                  loc=(1.1, 0.05),\n",
    "                  labelspacing=0.1)\n",
    "plt.setp(legend.get_texts(), \n",
    "         fontsize='large') \n",
    "plt.grid(True)\n",
    "# plt.savefig('tongshi.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>clusters</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">渠道代号</th>\n",
       "      <th>count</th>\n",
       "      <td>154</td>\n",
       "      <td>313</td>\n",
       "      <td>349</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>154</td>\n",
       "      <td>313</td>\n",
       "      <td>349</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>A203</td>\n",
       "      <td>A1</td>\n",
       "      <td>A108</td>\n",
       "      <td>A118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">素材类型</th>\n",
       "      <th>count</th>\n",
       "      <td>154</td>\n",
       "      <td>313</td>\n",
       "      <td>349</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>jpg</td>\n",
       "      <td>swf</td>\n",
       "      <td>jpg</td>\n",
       "      <td>swf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>153</td>\n",
       "      <td>276</td>\n",
       "      <td>345</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">广告类型</th>\n",
       "      <th>count</th>\n",
       "      <td>154</td>\n",
       "      <td>313</td>\n",
       "      <td>349</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>banner</td>\n",
       "      <td>不确定</td>\n",
       "      <td>横幅</td>\n",
       "      <td>tips</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>154</td>\n",
       "      <td>313</td>\n",
       "      <td>348</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">合作方式</th>\n",
       "      <th>count</th>\n",
       "      <td>154</td>\n",
       "      <td>313</td>\n",
       "      <td>349</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>cpc</td>\n",
       "      <td>roi</td>\n",
       "      <td>cpc</td>\n",
       "      <td>cpm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>150</td>\n",
       "      <td>238</td>\n",
       "      <td>342</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">广告尺寸</th>\n",
       "      <th>count</th>\n",
       "      <td>154</td>\n",
       "      <td>313</td>\n",
       "      <td>349</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>308*388</td>\n",
       "      <td>600*90</td>\n",
       "      <td>600*90</td>\n",
       "      <td>450*300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>104</td>\n",
       "      <td>312</td>\n",
       "      <td>333</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">广告卖点</th>\n",
       "      <th>count</th>\n",
       "      <td>154</td>\n",
       "      <td>313</td>\n",
       "      <td>349</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>满减</td>\n",
       "      <td>打折</td>\n",
       "      <td>直降</td>\n",
       "      <td>打折</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>119</td>\n",
       "      <td>169</td>\n",
       "      <td>248</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "clusters           0       1       2        3\n",
       "渠道代号 count       154     313     349       73\n",
       "     unique      154     313     349       73\n",
       "     top        A203      A1    A108     A118\n",
       "     freq          1       1       1        1\n",
       "素材类型 count       154     313     349       73\n",
       "     unique        2       2       2        2\n",
       "     top         jpg     swf     jpg      swf\n",
       "     freq        153     276     345       72\n",
       "广告类型 count       154     313     349       73\n",
       "     unique        1       1       2        1\n",
       "     top      banner     不确定      横幅     tips\n",
       "     freq        154     313     348       73\n",
       "合作方式 count       154     313     349       73\n",
       "     unique        2       4       3        4\n",
       "     top         cpc     roi     cpc      cpm\n",
       "     freq        150     238     342       43\n",
       "广告尺寸 count       154     313     349       73\n",
       "     unique        3       2       4        2\n",
       "     top     308*388  600*90  600*90  450*300\n",
       "     freq        104     312     333       54\n",
       "广告卖点 count       154     313     349       73\n",
       "     unique        5       5       6        5\n",
       "     top          满减      打折      直降       打折\n",
       "     freq        119     169     248       38"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = merge_data[['渠道代号', '素材类型', '广告类型', '合作方式', '广告尺寸', '广告卖点', 'clusters']]\n",
    "df3 = df2.groupby('clusters').describe(include='all').T\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>clusters</th>\n",
       "      <th>渠道代号</th>\n",
       "      <th>素材类型</th>\n",
       "      <th>广告类型</th>\n",
       "      <th>合作方式</th>\n",
       "      <th>广告尺寸</th>\n",
       "      <th>广告卖点</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>A203</td>\n",
       "      <td>jpg</td>\n",
       "      <td>banner</td>\n",
       "      <td>cpc</td>\n",
       "      <td>308*388</td>\n",
       "      <td>满减</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>A1</td>\n",
       "      <td>swf</td>\n",
       "      <td>不确定</td>\n",
       "      <td>roi</td>\n",
       "      <td>600*90</td>\n",
       "      <td>打折</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>A108</td>\n",
       "      <td>jpg</td>\n",
       "      <td>横幅</td>\n",
       "      <td>cpc</td>\n",
       "      <td>600*90</td>\n",
       "      <td>直降</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>A118</td>\n",
       "      <td>swf</td>\n",
       "      <td>tips</td>\n",
       "      <td>cpm</td>\n",
       "      <td>450*300</td>\n",
       "      <td>打折</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   clusters  渠道代号 素材类型    广告类型 合作方式     广告尺寸 广告卖点\n",
       "0         0  A203  jpg  banner  cpc  308*388   满减\n",
       "1         1    A1  swf     不确定  roi   600*90   打折\n",
       "2         2  A108  jpg      横幅  cpc   600*90   直降\n",
       "3         3  A118  swf    tips  cpm  450*300   打折"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.groupby('clusters').agg(lambda x: x.value_counts().index[0]).reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据结论"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 资源倾斜策略：\n",
    "   - 优先扩大类别1（CPC+Banner+满减）的投放规模，其注册率和转化率双高\n",
    "   - 对类别2（高UV群体）优化落地页体验，提升600 * 90尺寸广告的转化效率\n",
    "\n",
    "2. 风险规避建议：\n",
    "   - 类别0（ROI结算）存在转化率风险，建议设置投放时长上限（当前平均17天）\n",
    "   - 类别3（Tips广告）表现平庸，可测试更换为Banner形式或调整尺寸为308 * 388\n",
    "\n",
    "3. A/B测试方向：\n",
    "   - 在类别0/3中测试JPG替代SWF素材的效果\n",
    "   - 对类别1尝试\"满赠\"替代\"满减\"促销策略"
   ]
  }
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